Adherence with prescribed medications in clinical practice as well as clinical trials is critical to the success of pharmacological interventions. In psychiatric disorders, clinical non-adherence rates range from 30-60%. Non-adherence is also a significant issue for elderly people with chronic diseases and becomes amplified when they have dementia associated with Alzheimer's disease (AD) and comorbid conditions such as cardiovascular disease. Beyond clinical practice, the issue of incomplete or sub-optimal adherence in clinical trials creates even greater problems in measuring efficacy of drugs where a significant placebo effect can complicate the measurement of efficacy.
Accurate measurement of adherence is particularly valuable in Phase II proof of concept trials where critical decisions about further clinical development of drugs need to be made. Non-adherence with the prescribed medication gives rise to unintended variability in actual drug exposure and introduces potentially powerful confounding effects in measurement of treatment effect. These issues with sub-optimal compliance indicate that mechanisms to accurately measure compliance during both clinical trials and during routine use of drugs could have a significant impact on the ability to prospectively exclude non-compliant patients in clinical trials or to assist caregivers in identification of non-compliant patients who are treated on an outpatient basis.
Adherence measurement tools typically are characterized as subjective (e.g., patient and/or caregiver recall of doses taken or missed) or objective (e.g. pill counts, direct observation, pharmacy refill records). Most evidence indicates that self-report adherence measures show only moderate correspondence to other adherence measures.
Objective compliance measures, such as pill counts and prescription refill history, are considered to be more valid, more reliable, and less influenced by social desirability and patient recall errors. However, these objective tools can be expensive and difficult to use correctly and may overestimate nonadherence. Electronic monitoring devices such as Medication Event Monitoring System (MEMS®) or Helping Hand® provide an objective measure with a microchip in the bottle or pill pack that prompts the patient to take the medication and stamps the time and date when the tablet was removed from the bottle or blister pack. Recent technological advances have also led to smartphones being employed to photograph or video record pill counts. Researchers have also developed ingestible biosensor system comprising a radio-frequency identification (RFID)-tagged gelatin capsule. Once the capsule dissolves in the stomach, the RFID tag activates to transmit a unique signal to a relay device which transmits a time-stamped message to a cloud-based server that functions as a direct measure of medication adherence (myTmed®). Furthermore, a constellation of mobile technologies that provide real-time direct measures of medication adherence have been developed including microchips being embedded in the capsule that communicate with a wearable device (Proteus or e-Tect®), facial recognition technology through smart phone (Aicure) and breath detection of a capsule tracer (Xhale Smart®). While these devices are more accurate than self-reports or pill counts, they are very expensive, require extensive subject training and compliance with technology, can raise subject anxiety and have the potential of increasing placebo response due to increased attention and ‘halo effect’ of the technology. Moreover many of these methods can be fooled by a patient holding it in the mouth to show compliance and then removing the pill without ingestion of the study medication. Therefore, a more definitive approach to measure compliance is through measurement of study drug or an ingestible biomarker that can definitively ensure that the patient ingested the study medication.
Several markers, such as riboflavin, quinine, and acetazolamide, have been investigated as markers to indicate adherence of a subject to the proscribed drug schedule, but all are unsuitable for use for long-term monitoring. (Ramanujam, V. M., et al. Riboflavin as an oral tracer for monitoring compliance in clinical research. Open Biomark J 2011, 1-7 (2011); Babalonis, S., et al. Quinine as a potential tracer for medication adherence: A pharmacokinetic and pharmacodynamic assessment of quinine alone and in combination with oxycodone in humans. J Clin Pharmacol 55, 1332-1343 (2015); and Hampson, A. J., et al. A Pharmacokinetic Study Examining Acetazolamide as a Novel Adherence Marker for Clinical Trials. J Clin Psychopharmacol 36, 324-332 (2016)). These markers have short detection windows that restricts the adherence measurement to as short a period as 24 hours in the case of riboflavin. These markers fail to detect “white coat compliance subjects” where the subjects are non-compliant until shortly before the clinic visit and return to non-compliant behavior following the clinic visit. Because riboflavin and quinine may be introduced through the subject's diet, they are unreliable indicators of adherence. Acetazolamide also has several negative side affects, such as drowsiness, tingling, loss of taste, confusion, and tiredness at therapeutic doses. As a result, these markers are unsuitable for long-term monitoring.
It would therefore constitute a major advance in the art to provide biomarker compositions and techniques that enable long-term retrospective determinations of dosing, dosage, and administration schedule compliance of active agents, e.g., for time periods on the order of 7-10 days and even longer.